Databricks-GenAI-Assoc Assembling and Deploying Apps Practice Question
Which workflow best describes the recommended CI/CD process for updating a Databricks Asset Bundle?
⚠ Common exam trap
Candidates often assume manual workspace changes are sufficient or forget the critical step of running 'bundle validate' before deployment, leading to syntax errors that only appear during the actual deployment process.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Update the local YAML, run 'bundle validate', and deploy to the target environment.
A robust CI/CD workflow for Databricks Asset Bundles involves validating the configuration, testing in a development workspace, and then deploying to production via an automated process. By validating the bundle before deployment, you catch syntax errors early. This pipeline-driven approach ensures that all changes are tracked in version control, reviewed through pull requests, and deployed consistently, minimizing the risks associated with manual workspace configuration changes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Make changes directly in the production workspace and export the updated YAML.
Why it's wrong here
Directly modifying production environments bypasses the CI/CD pipeline and prevents proper testing. Changes should always originate in version control, be merged through a code review process, and then deployed programmatically. This ensures that the production state matches the source of truth stored in your repository.
- ✓
Update the local YAML, run 'bundle validate', and deploy to the target environment.
Why this is correct
This workflow follows standard DevOps practices: updating the configuration, validating it to ensure schema compliance, and deploying. Using the CLI ensures that the deployment process is repeatable and documented. Validation catches errors before they impact the environment, ensuring a smoother update process in production workspaces.
- ✗
Use the Databricks UI to update the Job definition and update the YAML manually later.
Why it's wrong here
Manual updates in the UI lead to configuration drift, where the workspace state diverges from the source-controlled bundle. Always update the configuration file first, then deploy it via the CLI. This keeps the repository as the primary source of truth for the entire infrastructure deployment.
- ✗
Deploy the bundle to production without validation to speed up the delivery time.
Why it's wrong here
Skipping validation increases the risk of deployment failure and service downtime. Validating the bundle is a fast, low-cost step that identifies potential configuration issues before they affect the production environment. Efficiency should not be prioritized over system stability and reliability in an automated CI/CD pipeline.
Visual reference
About these practice questions
This Databricks-GenAI-Assoc question is part of Courseiva's 330-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
This Databricks-GenAI-Assoc practice question is part of Courseiva's free Databricks certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Databricks-GenAI-Assoc exam.